Hello tabular data: Deploy a model and requesting a prediction

After your AutoML tabular classification model is done training, create an endpoint and deploy your model to the endpoint. After your model is deployed to this new endpoint, test your model by requesting a prediction.

This tutorial has several pages:

  1. Setting up your project and environment.

  2. Creating a tabular dataset and training an AutoML classification model.

  3. Deploying the model to an endpoint and sending a prediction.

  4. Cleaning up your project.

Each page assumes that you have already performed the instructions from the previous pages of the tutorial.

Deploy your model to an endpoint

When your model finishes training, it is listed in the Models tab.

  1. In the Google Cloud Console, in the Vertex AI section, go to the Models page.

    Go to the Models page

  2. Find your model, and click its link to open its Evaluate panel.

    This panel displays quality metrics for the model, including a confusion matrix. You can select a value for the target column to see evaluation metrics for that value. Below, you can see how strongly each column affected model training (Feature importance).

  3. Open the Deploy & Test panel. Under Deploy your model, click Deploy to endpoint.

  4. Enter Structured_AutoML_Tutorial for the endpoint name and select the model you just created.

    Accept the defaults for traffic split, minimum and maximum number of compute nodes, and the machine type.

  5. Click Continue, then click Deploy to create your endpoint and deploy your model to it.

    Deploying a model can take several minutes.

Request a prediction

  1. While the endpoint is being created, you can optionally enter a set of values for a prediction. Return to the Models list in the left-hand navigation panel and open your newly created model.

  2. Open the Deploy & test tab.

    You can use the prefilled values for the prediction data or enter your own.

  3. When the model is deployed, click Predict.

    For this model, a prediction result of 1 represents a negative outcome—a deposit is not made at the bank. A prediction result of 2 represents a positive outcome—a deposit is made at the bank.

    If you used the pre-filled prediction values, the local feature importance values are all zero. This is because the pre-filled values are the baseline prediction data, so the prediction returned is the baseline prediction value.

What's next

Follow the last page of the tutorial to clean up resources that you have created.